Microbial Food Web Models

Microbial biogeochemical models contain a large number of parameters
that are needed to characterize microbial growth kinetics, such as
maximum specific growth rate, half saturation constants and growth
efficiency.

In Vallino
(2000) we used data from a mesocosm experiment to estimate
parameters in a microbial food web model used to simulate how bacteria
use dissolved and particulate organic matter.

Microbial
food web model focused on dissolved organic matter (DOM) utilized by
bacteria (i.e., osmotrophs). The model uses 10 state
variables to track both C and N flows, and has 29 unknown kinetic
parameters. In addition, the initial conditions of the 10
state variables needed to be determined. (Click on image for larger picture.)

Mesocosm
experimental treatments included (A) control (no additions), (B)
organic matter addition, (C) nutrient addition and (D) organic matter
plus nutrient addition. Microcoms were run for 21 days where
nutrients concentrations and bacterial and primary productivity were
measured daily. See here for more information. (Click on image for larger picture.)

Example of model fit to data collected from treatment
D. Blue line shows initial model simulation based on
literature values for parameters, while black line shows model fit
after data assimilation. (Click on image for larger picture.)

As described in the manuscript we tested numerous optimization routines
including both local and global optimization solvers (also see NEOS Optimization Software Guide).
Interestingly,
we found different solvers uncovered different parameter values that
produced
very similar model fits, which indicates unique solutions do
not exist. Furthermore, we demonstrate that these types of
kinetic-based food web models do not extrapolate well beyond the data
they are calibrated with. The model can fit data from any one microcosm
treatment, but does poorly when compared to data from other treatments not used in data
assimilation. It appears that the large number of degrees of freedom
allow the model to hide structural errors in the model equations that
are only revealed when testing the model against new data.